Take the Full Course of Datawarehouse
What we Provide
1)22 Videos (Index is given down) + Update will be Coming Before final exams
2)Hand made Notes with problems for your to practice
3)Strategy to ScoreGoodMarks in DWM
To buy the course click here: https://goo.gl/to1yMH
or Fill the form we will contact you
https://goo.gl/forms/2SO5NAhqFnjOiWvi2
if you have any query email us at
support@lastmomenttuitions.com
or
lastmomenttuitions@gmail.com
Index
Introduction to Datawarehouse
Meta data in 5 mins
Datamart in datawarehouse
Architecture of datawarehouse
how to draw star schema slowflake schema and fact constelation
what is Olap operation
OLAP vs OLTP
decision tree with solved example
K mean clustering algorithm
Introduction to data mining and architecture
Naive bayes classifier
Apriori AlgorithmAgglomerative clustering algorithmn
KDD in data mining
ETL process
FP TREE Algorithm
Decision tree

published:25 Apr 2017

views:305162

** PythonTraining for Data Science: https://www.edureka.co/python **
This Edureka Machine Learning tutorial (Machine Learning Tutorial with Python Blog: https://goo.gl/fe7ykh ) series presents another video on "K-Means Clustering Algorithm". Within the video you will learn the concepts of K-Means clustering and its implementation using python. Below are the topics covered in today's session:
1. What is Clustering?
2. Types of Clustering
3. What is K-Means Clustering?
4. How does a K-Means Algorithm works?
5. K-Means Clustering Using Python
Machine Learning Tutorial Playlist: https://goo.gl/UxjTxm
Subscribe to our channel to get video updates. Hit the subscribe button above.
How it Works?
1. This is a 5 WeekInstructor led OnlineCourse,40 hours of assignment and 20 hours of project work
2. We have a 24x7 One-on-One LIVETechnical Support to help you with any problems you might face or any clarifications you may require during the course.
3. At the end of the training you will be working on a real time project for which we will provide you a Grade and a VerifiableCertificate!
- - - - - - - - - - - - - - - - -
About the Course
Edureka's Python Online Certification Training will make you an expert in Python programming. It will also help you learn Python the Big data way with integration of Machine learning, Pig, Hive and Web Scraping through beautiful soup. During our Python Certification training, our instructors will help you:
1. Programmatically download and analyze data
2. Learn techniques to deal with different types of data – ordinal, categorical, encoding
3. Learn data visualization
4. Using I python notebooks, master the art of presenting step by step data analysis
5. Gain insight into the 'Roles' played by a Machine Learning Engineer
6. Describe Machine Learning
7. Work with real-time data
8. Learn tools and techniques for predictive modeling
9. Discuss Machine Learning algorithms and their implementation
10. Validate Machine Learning algorithms
11. Explain Time Series and its related concepts
12. Perform Text Mining and Sentimental analysis
13. Gain expertise to handle business in future, living the present
- - - - - - - - - - - - - - - - - - -
Why learn Python?
Programmers love Python because of how fast and easy it is to use. Python cuts development time in half with its simple to read syntax and easy compilation feature. Debugging your programs is a breeze in Python with its built in debugger. Using Python makes Programmers more productive and their programs ultimately better. Python continues to be a favorite option for data scientists who use it for building and using Machine learning applications and other scientific computations.
Python runs on Windows, Linux/Unix, Mac OS and has been ported to Java and .NET virtual machines. Python is free to use, even for the commercial products, because of its OSI-approved open source license.
Python has evolved as the most preferred Language for Data Analytics and the increasing search trends on python also indicates that Python is the next "Big Thing" and a must for Professionals in the Data Analytics domain.
For more information, please write back to us at sales@edureka.co
Call us at US: +18336900808 (Toll Free) or India: +918861301699
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
CustomerReview
Sairaam Varadarajan, Data Evangelist at Medtronic, Tempe, Arizona: "I took Big Data and Hadoop / Python course and I am planning to take Apache Mahout thus becoming the "customer of Edureka!". Instructors are knowledge... able and interactive in teaching. The sessions are well structured with a proper content in helping us to dive into Big Data / Python. Most of the online courses are free, edureka charges a minimal amount. Its acceptable for their hard-work in tailoring - All new advanced courses and its specific usage in industry. I am confident that, no other website which have tailored the courses like Edureka. It will help for an immediate take-off in Data Science and Hadoop working."

published:28 May 2018

views:20328

In this video I describe how the k Nearest Neighbors algorithm works, and provide a simple example using 2-dimensional data and k = 3.
This presentation is available at: http://prezi.com/ukps8hzjizqw/?utm_campaign=share&utm_medium=copy

published:18 Feb 2014

views:378856

Here's a quick explanation of Kadane's Algorithm to Maximum Sum Subarray Problem.
This problem, also known as Maximum Subarray Problem, is a very common question in a coding interview, and this gives the optimal answer.

K-means sorts data based on averages. Dr MikePound explains how it works.
FirePong in Detail: https://youtu.be/ZoZMMg1r_Oc
Deep Dream: https://youtu.be/BsSmBPmPeYQ
FPS & Digital Video: https://youtu.be/yniSnYtkrwQ
Dr. Mike's Code:
% This script is the one mentioned during the Computerphile Image
% Segmentation video. I chose matlab because it's a popular tool for
% quickly prototyping things. Matlab licenses are pricey, if you don't have
% one (or, like me, work for an organisation that does) try Octave as a
% good free alternative. This code should work in Octave too.
% Load in an input image
im = imread('C:\Path\Of\Input\Image.jpg');
% In matlab, K-means operates on a 2D array, where each sample is one row,
% and the features are the columns. We can use the reshape function to turn
% the image into this format, where each pixel is one row, and R,G and B
% are the columns. We are turning a W,H,3 image into W*H,3
% We also cast to a double array, because K-means requires it in matlab
imflat = double(reshape(im, size(im,1) * size(im,2), 3));
% I specify that initialisation shuold sample points at
% random, rather than anything complex like kmeans++ initialisation.
% Kmeans++ takes a long time if you are using 256 classes.
% Perform k-means. This function returns the class IDs assigned to each
% pixel, and in this case we also want the mean values for each class -
% what colour is each class. This can take a long time if the value for K
% is large, I've used the sampling start strategy to speed things up.
% While KMeans is running, it will show you the iteration count, and the
% number of pixels that have changed class since last iteration. This
% number should get lower and lower, as the means settle on appropriate
% values. For large K, it's unlikely that we will ever reach zero movement
% (convergence) within 150 iterations.
K = 3
[kIDs, kC] = kmeans(imflat, K, 'Display', 'iter', 'MaxIter', 150, 'Start', 'sample');
% Matlab can output paletted images, that is, grayscale images where the
% colours are stored in a separate array. This array is kC, and kIDs are
% the grayscale indices.
colormap = kC / 256; % Scale0-1, since this is what matlab wants
% Reshape kIDs back into the original image shape
imout = reshape(uint8(kIDs), size(im,1), size(im,2));
% Save file out, you need to subtract 1 from the image classes, since once
% stored in the file the values should go from 0-255, not 1-256 like matlab
% would do.
imwrite(imout - 1, colormap, 'C:\Path\Of\Output\Image.png');
http://www.facebook.com/computerphile
https://twitter.com/computer_phile
This video was filmed and edited by Sean Riley.
Computer Science at the University of Nottingham: http://bit.ly/nottscomputer
Computerphile is a sister project to Brady Haran's Numberphile. More at http://www.bradyharan.com

published:14 Sep 2016

views:164914

This K Means clustering algorithm tutorial video will take you through machine learning basics, types of clustering algorithms, what is K Means clustering, how does K Means clustering work with examples along with a demo in python on K-Means clustering - color compression. This MachineLearning algorithm tutorial video is ideal for beginners to learn how K Means clustering work.
Below topics are covered in this K-Means Clustering Algorithm Tutorial:
1. Types of Machine Learning? ( 07:08 )
2. What is K Means Clustering? ( 00:10 )
3. Applications of K Means Clustering ( 09:27 )
4. Common distance measure ( 10:20 )
5. How does K Means Clustering work? ( 12:27 )
6. K Means Clustering Algorithm ( 20:08 )
7. Demo In Python: K Means Clustering ( 26:20 )
8. Use case: Color compression In Python ( 38:38 )
What is Machine Learning: Machine Learning is an application of Artificial Intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed.
Subscribe to our channel for more Machine Learning Tutorials: https://www.youtube.com/user/Simplilearn?sub_confirmation=1
You can also go through the Slides here: https://goo.gl/B6k4R6
Machine Learning Articles: https://www.simplilearn.com/what-is-artificial-intelligence-and-why-ai-certification-article?utm_campaign=Kmeans-Clustering-Algorithm-Xvwt7y2jf5E&utm_medium=Tutorials&utm_source=youtube
To gain in-depth knowledge of Machine Learning, check our Machine Learning certification training course: https://www.simplilearn.com/big-data-and-analytics/machine-learning-certification-training-course?utm_campaign=Kmeans-Clustering-Algorithm-Xvwt7y2jf5E&utm_medium=Tutorials&utm_source=youtube
#MachineLearningAlgorithms #Datasciencecourse #DataScience #SimplilearnMachineLearning #MachineLearningCourse
- - - - - - - -
About Simplilearn Machine Learning course:
A form of artificial intelligence, Machine Learning is revolutionizing the world of computing as well as all people’s digital interactions. Machine Learning powers such innovative automated technologies as recommendation engines, facial recognition, fraud protection and even self-driving cars.This Machine Learning course prepares engineers, data scientists and other professionals with knowledge and hands-on skills required for certification and job competency in Machine Learning.
- - - - - - -
Why learn Machine Learning?
Machine Learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of Machine Learning
The Machine Learning market size is expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022, at a CompoundAnnualGrowth Rate (CAGR) of 44.1% during the forecast period.
- - - - - -
What skills will you learn from this Machine Learning course?
By the end of this Machine Learning course, you will be able to:
1. Master the concepts of supervised, unsupervised and reinforcement learning concepts and modeling.
2. Gain practical mastery over principles, algorithms, and applications of Machine Learning through a hands-on approach which includes working on 28 projects and one capstone project.
3. Acquire thorough knowledge of the mathematical and heuristic aspects of Machine Learning.
4. Understand the concepts and operation of support vector machines, kernel SVM, naive bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbors, K-means clustering and more.
5. Be able to model a wide variety of robust Machine Learning algorithms including deep learning, clustering, and recommendation systems
- - - - - - -
Who should take this Machine Learning TrainingCourse?
We recommend this Machine Learning training course for the following professionals in particular:
1. Developers aspiring to be a data scientist or Machine Learning engineer
2. Information architects who want to gain expertise in Machine Learning algorithms
3. Analytics professionals who want to work in Machine Learning or artificial intelligence
4. Graduates looking to build a career in data science and Machine Learning
- - - - - -
For more updates on courses and tips follow us on:
- Facebook: https://www.facebook.com/Simplilearn
- Twitter: https://twitter.com/simplilearn
- LinkedIn: https://www.linkedin.com/company/simplilearn
- Website: https://www.simplilearn.com
Get the Android app: http://bit.ly/1WlVo4u
Get the iOS app: http://apple.co/1HIO5J0

published:19 Mar 2018

views:16876

.
CopyrightDisclaimer Under Section 107 of the Copyright Act 1976, allowance is made for "FAIRUSE" for purposes such as criticism, comment, news reporting, teaching, scholarship, and research. Fair use is a use permitted by copyright statute that might otherwise be infringing. Non-profit, educational or personal use tips the balance in favor of fair use.
.

( Data ScienceTraining - https://www.edureka.co/data-science )
This Edureka k-means clustering algorithm tutorial video (Data Science Blog Series: https://goo.gl/6ojfAa) will take you through the machine learning introduction, cluster analysis, types of clustering algorithms, k-means clustering, how it works along with an example/ demo in R. This Data Science with R tutorial video is ideal for beginners to learn how k-means clustering work. You can also read the blog here: https://goo.gl/QM8on4
Subscribe to our channel to get video updates. Hit the subscribe button above.
Check our complete Data Science playlist here: https://goo.gl/60NJJS
#kmeans #clusteranalysis #clustering #datascience #machinelearning
How it Works?
1. There will be 30 hours of instructor-led interactive online classes, 40 hours of assignments and 20 hours of project
2. We have a 24x7 One-on-One LIVETechnical Support to help you with any problems you might face or any clarifications you may require during the course.
3. You will get LifetimeAccess to the recordings in the LMS.
4. At the end of the training you will have to complete the project based on which we will provide you a VerifiableCertificate!
- - - - - - - - - - - - - -
About the Course
Edureka's Data Science course will cover the whole data life cycle ranging from Data Acquisition and Data Storage using R-Hadoop concepts, Applying modelling through R programming using Machine learning algorithms and illustrate impeccable Data Visualization by leveraging on 'R' capabilities.
- - - - - - - - - - - - - -
Why Learn Data Science?
Data Science training certifies you with ‘in demand’ Big Data Technologies to help you grab the top paying Data Science job title with Big Data skills and expertise in R programming, Machine Learning and Hadoop framework.
After the completion of the DataScience course, you should be able to:
1. Gain insight into the 'Roles' played by a DataScientist
2. Analyse Big Data using R, Hadoop and Machine Learning
3. Understand the Data AnalysisLife Cycle
4. Work with different data formats like XML, CSV and SAS, SPSS, etc.
5. Learn tools and techniques for data transformation
6. Understand Data Mining techniques and their implementation
7. Analyse data using machine learning algorithms in R
8. Work with Hadoop Mappers and Reducers to analyze data
9. Implement various Machine Learning Algorithms in Apache Mahout
10. Gain insight into data visualization and optimization techniques
11. Explore the parallel processing feature in R
- - - - - - - - - - - - - -
Who should go for this course?
The course is designed for all those who want to learn machine learning techniques with implementation in R language, and wish to apply these techniques on Big Data. The following professionals can go for this course:
1. Developers aspiring to be a 'Data Scientist'
2. Analytics Managers who are leading a team of analysts
3. SAS/SPSS Professionals looking to gain understanding in Big Data Analytics
4. Business Analysts who want to understand Machine Learning (ML) Techniques
5. InformationArchitects who want to gain expertise in Predictive Analytics
6. 'R' professionals who want to captivate and analyze Big Data
7. Hadoop Professionals who want to learn R and ML techniques
8. Analysts wanting to understand Data Science methodologies
Please write back to us at sales@edureka.co or call us at +918880862004 or 18002759730 for more information.
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
Customer Reviews:
Gnana Sekhar Vangara, TechnologyLead at WellsFargo.com, says, "Edureka Data science course provided me a very good mixture of theoretical and practical training. The training course helped me in all areas that I was previously unclear about, especially concepts like Machine learning and Mahout. The training was very informative and practical. LMS pre recorded sessions and assignmemts were very good as there is a lot of information in them that will help me in my job. The trainer was able to explain difficult to understand subjects in simple terms. Edureka is my teaching GURU now...Thanks EDUREKA and all the best. "

published:01 Mar 2017

views:58809

This week, Ingo Mierswa, RapidMiner's CEO, Co-Founder & DataScientist in Residence, explains how the K-nearest-neighbors algorithm is used to formulate ideas by comparing points in a data-space and using the most similar data points as guidelines for predictions.
Plus, Ingo asks Data Scientist Number 7 to clean up Glen's mess, Doc Brown makes another split-second appearance and we hear the brief chuckle of a Python wearing a tuxedo.
DISCLAIMER: No data scientists were harmed during the filming of this episode. In fact, they were too busy doing amazing things with RapidMiner.
MUSIC CREDITS: Evil Thoughts, The Oscillator, James Taylor Quartet, Real SelfRecords, 2000

Machine Learning (journal)

In 2001, forty editors and members of the editorial board of Machine Learning resigned in order to support the Journal of Machine Learning Research (JMLR), saying that in the era of the internet, it was detrimental for researchers to continue publishing their papers in expensive journals with pay-access archives. Instead, they wrote, they supported the model of JMLR, in which authors retained copyright over their papers and archives were freely available on the internet.

In relation to logic-based and artificial neural network-based clinical decision support system, which are also computer applications to the medical decision making field, algorithms are less complex in architecture, data structure and user interface. Medical algorithms are not necessarily implemented using digital computers. In fact, many of them can be represented on paper, in the form of diagrams, nomographs, etc.

Algorithm (My Heart to Fear album)

Critical reception

Awarding the album three stars from Alternative Press, Jason Schreurs writes, "As is the case with the bulk of this musical style, the vocals bring it back down to a near-mediocre level." Bradley Zorgdrager, rating the album a five out of ten for Exclaim!, says, "Unfortunately, a lack of inspiration causes the songs to come undone, as many of the parts sound only like a means to get to the next." Giving the album four stars at About.com, Todd Lyons states, "everything binds together into one masterful meditation." Tim Dodderidge, indicating in a 8.5 out of ten review by Mind Equals Blown, writes, "From start to finish, My Heart to Fear’s debut full-length is an energetic, ferocious, cathartic and inspiring metal album."

Kevin Hoskins, giving the album three and a half stars for Jesus Freak Hideout, describes, "this is just metal done well ... but any hardcore fan will be digging this release all summer long." Awarding the album four and a half stars from HM Magazine, Sean Huncherick states, "One good thing about Algorithm is that the band realizes they don’t need to constantly play as fast as they can." Brody B., rating the album four star at Indie Vision Music, writes, "With a few minor tweaks here and there that could have made songs feel more fleshed out I would have had a hard time finding fault with this debut record."

Machine

A machine is a tool containing one or more parts that uses energy to perform an intended action. Machines are usually powered by mechanical, chemical, thermal, or electrical means, and are often motorized. Historically, a power tool also required moving parts to classify as a machine. However, the advent of electronics has led to the development of power tools without moving parts that are considered machines.

Penicillin (band)

History

Formed by friends at Tokai University in Tokyo, Japan on February 14, 1992. Penicillin began with Chisato on vocals, Gisho on bass, O-Jiro on drums and both Yuuji and Shaisuke on guitar. Before anything was recorded however the band's line-up changed to Hakuei on vocals, Gisho on bass, O-Jiro on drums and both Shaisuke and Chisato on guitar. Their name was taken from the punk rock group "Penicillin Shock" in the manga series To-y and they titled their first album, which was released in 1994 and produced by Kiyoshi of Media Youth, after the fictional band. However, after their first album, Shaisuke left Penicillin to join Deshabillz.

The band made their major debut in 1996 with "Blue Moon" on Pioneer LDC. In 1997 the released Limelight, which was named one of the top albums from 1989-1998 in a 2004 issue of the music magazine Band Yarouze. They stayed on Pioneer until 1998 when they changed to East West Japan. Their sixth single released, "Romance", released January 15, 1998 ranked within the top 10 on the Oricon charts for six consecutive weeks, selling over 900,000 copies. It was the opening theme of the Sexy Commando Gaiden. After they were dropped by East West Japan they changed to Omega A.T. Music (an indie label) in 2001. Omega happens to be under Omega Project Holdings which Yoshiaki Kondou (Gisho) is the CEO of. However they were only on Omega for a year before being picked up by Hiboom, which was an indie subsidiary of Avex Trax. In 2005 when Hiboom was closed, they were picked up directly by Avex Trax and were once again on a major label.

Early life and career

Dell'Abate was born and raised in Uniondale, New York, on Long Island. He comes from a large Italian-American family. His father, Salvatore Dell'Abate, was an ice cream salesman, while his mother sold cooking items such as frying pans at the local supermarket. Gary attended Adelphi University, receiving the Richard F. Clemo Award his senior year, and he interned at several radio stations including WLIR. While interning with Roz Frank, a traffic reporter on WNBC, he came into contact with Howard Stern.

The Howard Stern Show

Dell'Abate has worked on The Howard Stern Show since September 4, 1984: originally on 66 WNBC, then syndicated through K-Rock in New York City, and later broadcast on Sirius XM Radio. Dell'Abate was originally hired for $150 a week, with duties including getting Stern's lunch and scheduling guests for the show.

K mean clustering algorithm with solve example

Take the Full Course of Datawarehouse
What we Provide
1)22 Videos (Index is given down) + Update will be Coming Before final exams
2)Hand made Notes with problems for your to practice
3)Strategy to ScoreGoodMarks in DWM
To buy the course click here: https://goo.gl/to1yMH
or Fill the form we will contact you
https://goo.gl/forms/2SO5NAhqFnjOiWvi2
if you have any query email us at
support@lastmomenttuitions.com
or
lastmomenttuitions@gmail.com
Index
Introduction to Datawarehouse
Meta data in 5 mins
Datamart in datawarehouse
Architecture of datawarehouse
how to draw star schema slowflake schema and fact constelation
what is Olap operation
OLAP vs OLTP
decision tree with solved example
K mean clustering algorithm
Introduction to data mining and architecture
Naive bayes classifier
Apriori AlgorithmAgglomerative clustering algorithmn
KDD in data mining
ETL process
FP TREE Algorithm
Decision tree

** PythonTraining for Data Science: https://www.edureka.co/python **
This Edureka Machine Learning tutorial (Machine Learning Tutorial with Python Blog: https://goo.gl/fe7ykh ) series presents another video on "K-Means Clustering Algorithm". Within the video you will learn the concepts of K-Means clustering and its implementation using python. Below are the topics covered in today's session:
1. What is Clustering?
2. Types of Clustering
3. What is K-Means Clustering?
4. How does a K-Means Algorithm works?
5. K-Means Clustering Using Python
Machine Learning Tutorial Playlist: https://goo.gl/UxjTxm
Subscribe to our channel to get video updates. Hit the subscribe button above.
How it Works?
1. This is a 5 WeekInstructor led OnlineCourse,40 hours of assignment and 20 hours of project work
2. We have a 24x7 One-on-One LIVETechnical Support to help you with any problems you might face or any clarifications you may require during the course.
3. At the end of the training you will be working on a real time project for which we will provide you a Grade and a VerifiableCertificate!
- - - - - - - - - - - - - - - - -
About the Course
Edureka's Python Online Certification Training will make you an expert in Python programming. It will also help you learn Python the Big data way with integration of Machine learning, Pig, Hive and Web Scraping through beautiful soup. During our Python Certification training, our instructors will help you:
1. Programmatically download and analyze data
2. Learn techniques to deal with different types of data – ordinal, categorical, encoding
3. Learn data visualization
4. Using I python notebooks, master the art of presenting step by step data analysis
5. Gain insight into the 'Roles' played by a Machine Learning Engineer
6. Describe Machine Learning
7. Work with real-time data
8. Learn tools and techniques for predictive modeling
9. Discuss Machine Learning algorithms and their implementation
10. Validate Machine Learning algorithms
11. Explain Time Series and its related concepts
12. Perform Text Mining and Sentimental analysis
13. Gain expertise to handle business in future, living the present
- - - - - - - - - - - - - - - - - - -
Why learn Python?
Programmers love Python because of how fast and easy it is to use. Python cuts development time in half with its simple to read syntax and easy compilation feature. Debugging your programs is a breeze in Python with its built in debugger. Using Python makes Programmers more productive and their programs ultimately better. Python continues to be a favorite option for data scientists who use it for building and using Machine learning applications and other scientific computations.
Python runs on Windows, Linux/Unix, Mac OS and has been ported to Java and .NET virtual machines. Python is free to use, even for the commercial products, because of its OSI-approved open source license.
Python has evolved as the most preferred Language for Data Analytics and the increasing search trends on python also indicates that Python is the next "Big Thing" and a must for Professionals in the Data Analytics domain.
For more information, please write back to us at sales@edureka.co
Call us at US: +18336900808 (Toll Free) or India: +918861301699
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
CustomerReview
Sairaam Varadarajan, Data Evangelist at Medtronic, Tempe, Arizona: "I took Big Data and Hadoop / Python course and I am planning to take Apache Mahout thus becoming the "customer of Edureka!". Instructors are knowledge... able and interactive in teaching. The sessions are well structured with a proper content in helping us to dive into Big Data / Python. Most of the online courses are free, edureka charges a minimal amount. Its acceptable for their hard-work in tailoring - All new advanced courses and its specific usage in industry. I am confident that, no other website which have tailored the courses like Edureka. It will help for an immediate take-off in Data Science and Hadoop working."

4:42

How kNN algorithm works

How kNN algorithm works

How kNN algorithm works

In this video I describe how the k Nearest Neighbors algorithm works, and provide a simple example using 2-dimensional data and k = 3.
This presentation is available at: http://prezi.com/ukps8hzjizqw/?utm_campaign=share&utm_medium=copy

11:17

Kadane's Algorithm to Maximum Sum Subarray Problem

Kadane's Algorithm to Maximum Sum Subarray Problem

Kadane's Algorithm to Maximum Sum Subarray Problem

Here's a quick explanation of Kadane's Algorithm to Maximum Sum Subarray Problem.
This problem, also known as Maximum Subarray Problem, is a very common question in a coding interview, and this gives the optimal answer.

K-means & Image Segmentation - Computerphile

K-means sorts data based on averages. Dr MikePound explains how it works.
FirePong in Detail: https://youtu.be/ZoZMMg1r_Oc
Deep Dream: https://youtu.be/BsSmBPmPeYQ
FPS & Digital Video: https://youtu.be/yniSnYtkrwQ
Dr. Mike's Code:
% This script is the one mentioned during the Computerphile Image
% Segmentation video. I chose matlab because it's a popular tool for
% quickly prototyping things. Matlab licenses are pricey, if you don't have
% one (or, like me, work for an organisation that does) try Octave as a
% good free alternative. This code should work in Octave too.
% Load in an input image
im = imread('C:\Path\Of\Input\Image.jpg');
% In matlab, K-means operates on a 2D array, where each sample is one row,
% and the features are the columns. We can use the reshape function to turn
% the image into this format, where each pixel is one row, and R,G and B
% are the columns. We are turning a W,H,3 image into W*H,3
% We also cast to a double array, because K-means requires it in matlab
imflat = double(reshape(im, size(im,1) * size(im,2), 3));
% I specify that initialisation shuold sample points at
% random, rather than anything complex like kmeans++ initialisation.
% Kmeans++ takes a long time if you are using 256 classes.
% Perform k-means. This function returns the class IDs assigned to each
% pixel, and in this case we also want the mean values for each class -
% what colour is each class. This can take a long time if the value for K
% is large, I've used the sampling start strategy to speed things up.
% While KMeans is running, it will show you the iteration count, and the
% number of pixels that have changed class since last iteration. This
% number should get lower and lower, as the means settle on appropriate
% values. For large K, it's unlikely that we will ever reach zero movement
% (convergence) within 150 iterations.
K = 3
[kIDs, kC] = kmeans(imflat, K, 'Display', 'iter', 'MaxIter', 150, 'Start', 'sample');
% Matlab can output paletted images, that is, grayscale images where the
% colours are stored in a separate array. This array is kC, and kIDs are
% the grayscale indices.
colormap = kC / 256; % Scale0-1, since this is what matlab wants
% Reshape kIDs back into the original image shape
imout = reshape(uint8(kIDs), size(im,1), size(im,2));
% Save file out, you need to subtract 1 from the image classes, since once
% stored in the file the values should go from 0-255, not 1-256 like matlab
% would do.
imwrite(imout - 1, colormap, 'C:\Path\Of\Output\Image.png');
http://www.facebook.com/computerphile
https://twitter.com/computer_phile
This video was filmed and edited by Sean Riley.
Computer Science at the University of Nottingham: http://bit.ly/nottscomputer
Computerphile is a sister project to Brady Haran's Numberphile. More at http://www.bradyharan.com

This K Means clustering algorithm tutorial video will take you through machine learning basics, types of clustering algorithms, what is K Means clustering, how does K Means clustering work with examples along with a demo in python on K-Means clustering - color compression. This MachineLearning algorithm tutorial video is ideal for beginners to learn how K Means clustering work.
Below topics are covered in this K-Means Clustering Algorithm Tutorial:
1. Types of Machine Learning? ( 07:08 )
2. What is K Means Clustering? ( 00:10 )
3. Applications of K Means Clustering ( 09:27 )
4. Common distance measure ( 10:20 )
5. How does K Means Clustering work? ( 12:27 )
6. K Means Clustering Algorithm ( 20:08 )
7. Demo In Python: K Means Clustering ( 26:20 )
8. Use case: Color compression In Python ( 38:38 )
What is Machine Learning: Machine Learning is an application of Artificial Intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed.
Subscribe to our channel for more Machine Learning Tutorials: https://www.youtube.com/user/Simplilearn?sub_confirmation=1
You can also go through the Slides here: https://goo.gl/B6k4R6
Machine Learning Articles: https://www.simplilearn.com/what-is-artificial-intelligence-and-why-ai-certification-article?utm_campaign=Kmeans-Clustering-Algorithm-Xvwt7y2jf5E&utm_medium=Tutorials&utm_source=youtube
To gain in-depth knowledge of Machine Learning, check our Machine Learning certification training course: https://www.simplilearn.com/big-data-and-analytics/machine-learning-certification-training-course?utm_campaign=Kmeans-Clustering-Algorithm-Xvwt7y2jf5E&utm_medium=Tutorials&utm_source=youtube
#MachineLearningAlgorithms #Datasciencecourse #DataScience #SimplilearnMachineLearning #MachineLearningCourse
- - - - - - - -
About Simplilearn Machine Learning course:
A form of artificial intelligence, Machine Learning is revolutionizing the world of computing as well as all people’s digital interactions. Machine Learning powers such innovative automated technologies as recommendation engines, facial recognition, fraud protection and even self-driving cars.This Machine Learning course prepares engineers, data scientists and other professionals with knowledge and hands-on skills required for certification and job competency in Machine Learning.
- - - - - - -
Why learn Machine Learning?
Machine Learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of Machine Learning
The Machine Learning market size is expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022, at a CompoundAnnualGrowth Rate (CAGR) of 44.1% during the forecast period.
- - - - - -
What skills will you learn from this Machine Learning course?
By the end of this Machine Learning course, you will be able to:
1. Master the concepts of supervised, unsupervised and reinforcement learning concepts and modeling.
2. Gain practical mastery over principles, algorithms, and applications of Machine Learning through a hands-on approach which includes working on 28 projects and one capstone project.
3. Acquire thorough knowledge of the mathematical and heuristic aspects of Machine Learning.
4. Understand the concepts and operation of support vector machines, kernel SVM, naive bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbors, K-means clustering and more.
5. Be able to model a wide variety of robust Machine Learning algorithms including deep learning, clustering, and recommendation systems
- - - - - - -
Who should take this Machine Learning TrainingCourse?
We recommend this Machine Learning training course for the following professionals in particular:
1. Developers aspiring to be a data scientist or Machine Learning engineer
2. Information architects who want to gain expertise in Machine Learning algorithms
3. Analytics professionals who want to work in Machine Learning or artificial intelligence
4. Graduates looking to build a career in data science and Machine Learning
- - - - - -
For more updates on courses and tips follow us on:
- Facebook: https://www.facebook.com/Simplilearn
- Twitter: https://twitter.com/simplilearn
- LinkedIn: https://www.linkedin.com/company/simplilearn
- Website: https://www.simplilearn.com
Get the Android app: http://bit.ly/1WlVo4u
Get the iOS app: http://apple.co/1HIO5J0

12:50

Lecture 60 — The k Means Algorithm | Stanford University

Lecture 60 — The k Means Algorithm | Stanford University

Lecture 60 — The k Means Algorithm | Stanford University

.
CopyrightDisclaimer Under Section 107 of the Copyright Act 1976, allowance is made for "FAIRUSE" for purposes such as criticism, comment, news reporting, teaching, scholarship, and research. Fair use is a use permitted by copyright statute that might otherwise be infringing. Non-profit, educational or personal use tips the balance in favor of fair use.
.

( Data ScienceTraining - https://www.edureka.co/data-science )
This Edureka k-means clustering algorithm tutorial video (Data Science Blog Series: https://goo.gl/6ojfAa) will take you through the machine learning introduction, cluster analysis, types of clustering algorithms, k-means clustering, how it works along with an example/ demo in R. This Data Science with R tutorial video is ideal for beginners to learn how k-means clustering work. You can also read the blog here: https://goo.gl/QM8on4
Subscribe to our channel to get video updates. Hit the subscribe button above.
Check our complete Data Science playlist here: https://goo.gl/60NJJS
#kmeans #clusteranalysis #clustering #datascience #machinelearning
How it Works?
1. There will be 30 hours of instructor-led interactive online classes, 40 hours of assignments and 20 hours of project
2. We have a 24x7 One-on-One LIVETechnical Support to help you with any problems you might face or any clarifications you may require during the course.
3. You will get LifetimeAccess to the recordings in the LMS.
4. At the end of the training you will have to complete the project based on which we will provide you a VerifiableCertificate!
- - - - - - - - - - - - - -
About the Course
Edureka's Data Science course will cover the whole data life cycle ranging from Data Acquisition and Data Storage using R-Hadoop concepts, Applying modelling through R programming using Machine learning algorithms and illustrate impeccable Data Visualization by leveraging on 'R' capabilities.
- - - - - - - - - - - - - -
Why Learn Data Science?
Data Science training certifies you with ‘in demand’ Big Data Technologies to help you grab the top paying Data Science job title with Big Data skills and expertise in R programming, Machine Learning and Hadoop framework.
After the completion of the DataScience course, you should be able to:
1. Gain insight into the 'Roles' played by a DataScientist
2. Analyse Big Data using R, Hadoop and Machine Learning
3. Understand the Data AnalysisLife Cycle
4. Work with different data formats like XML, CSV and SAS, SPSS, etc.
5. Learn tools and techniques for data transformation
6. Understand Data Mining techniques and their implementation
7. Analyse data using machine learning algorithms in R
8. Work with Hadoop Mappers and Reducers to analyze data
9. Implement various Machine Learning Algorithms in Apache Mahout
10. Gain insight into data visualization and optimization techniques
11. Explore the parallel processing feature in R
- - - - - - - - - - - - - -
Who should go for this course?
The course is designed for all those who want to learn machine learning techniques with implementation in R language, and wish to apply these techniques on Big Data. The following professionals can go for this course:
1. Developers aspiring to be a 'Data Scientist'
2. Analytics Managers who are leading a team of analysts
3. SAS/SPSS Professionals looking to gain understanding in Big Data Analytics
4. Business Analysts who want to understand Machine Learning (ML) Techniques
5. InformationArchitects who want to gain expertise in Predictive Analytics
6. 'R' professionals who want to captivate and analyze Big Data
7. Hadoop Professionals who want to learn R and ML techniques
8. Analysts wanting to understand Data Science methodologies
Please write back to us at sales@edureka.co or call us at +918880862004 or 18002759730 for more information.
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
Customer Reviews:
Gnana Sekhar Vangara, TechnologyLead at WellsFargo.com, says, "Edureka Data science course provided me a very good mixture of theoretical and practical training. The training course helped me in all areas that I was previously unclear about, especially concepts like Machine learning and Mahout. The training was very informative and practical. LMS pre recorded sessions and assignmemts were very good as there is a lot of information in them that will help me in my job. The trainer was able to explain difficult to understand subjects in simple terms. Edureka is my teaching GURU now...Thanks EDUREKA and all the best. "

This week, Ingo Mierswa, RapidMiner's CEO, Co-Founder & DataScientist in Residence, explains how the K-nearest-neighbors algorithm is used to formulate ideas by comparing points in a data-space and using the most similar data points as guidelines for predictions.
Plus, Ingo asks Data Scientist Number 7 to clean up Glen's mess, Doc Brown makes another split-second appearance and we hear the brief chuckle of a Python wearing a tuxedo.
DISCLAIMER: No data scientists were harmed during the filming of this episode. In fact, they were too busy doing amazing things with RapidMiner.
MUSIC CREDITS: Evil Thoughts, The Oscillator, James Taylor Quartet, Real SelfRecords, 2000

7:25

K means Clustering Algorithm Explained With an Example Easiest And Quickest Way Ever In Hindi

K means Clustering Algorithm Explained With an Example Easiest And Quickest Way Ever In Hindi

K means Clustering Algorithm Explained With an Example Easiest And Quickest Way Ever In Hindi

This KNNAlgorithm tutorial (K-Nearest NeighborClassification Algorithm tutorial) will help you understand what is KNN, why do we need KNN, how do we choose the factor 'K', when do we use KNN, how does KNN algorithm work and you will also see a use case demo showing how to predict whether a person will have diabetes or not using KNN algorithm. KNN algorithm can be applied to both classification and regression problems. Apparently, within the DataScience industry, it's more widely used to solve classification problems. It’s a simple algorithm that stores all available cases and classifies any new cases by taking a majority vote of its k neighbors. Now lets deep dive into this video to understand what is KNN algorithm and how does it actually works.
Below topics are explained in this K-Nearest Neighbor Classification Algorithm (KNN Algorithm) tutorial:
1. Why do we need KNN?
2. What is KNN?
3. How do we choose the factor 'K'?
4. When do we use KNN?
5. How does KNN algorithm work?
6. Use case - Predict whether a person will have diabetes or not
To learn more about Machine Learning, subscribe to our YouTube channel: https://www.youtube.com/user/Simplilearn?sub_confirmation=1
You can also go through the slides here: https://goo.gl/XP6xcp
Watch more videos on Machine Learning: https://www.youtube.com/watch?v=7JhjINPwfYQ&list=PLEiEAq2VkUULYYgj13YHUWmRePqiu8Ddy
#MachineLearningAlgorithms #Datasciencecourse #datascience #SimplilearnMachineLearning #MachineLearningCourse
Simplilearn’s Machine Learning course will make you an expert in Machine Learning, a form of Artificial Intelligence that automates data analysis to enable computers to learn and adapt through experience to do specific tasks without explicit programming. You will master Machine Learning concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, hands-on modeling to develop algorithms and prepare you for the role of Machine Learning Engineer
Why learn Machine Learning?
Machine Learning is rapidly being deployed in all kinds of industries, creating a huge demand for skilled professionals. The MachineLearning market size is expected to grow from USD 1.03 billion in 2016 to USD 8.81 billion by 2022, at a CompoundAnnualGrowth Rate (CAGR) of 44.1% during the forecast period.
You can gain in-depth knowledge of Machine Learning by taking our Machine Learning certification training course. With Simplilearn’s Machine Learning course, you will prepare for a career as a Machine Learning engineer as you master concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, and hands-on modeling to develop algorithms. Those who complete the course will be able to:
1. Master the concepts of supervised, unsupervised and reinforcement learning concepts and modeling.
2. Gain practical mastery over principles, algorithms, and applications of Machine Learning through a hands-on approach which includes working on 28 projects and one capstone project.
3. Acquire thorough knowledge of the mathematical and heuristic aspects of Machine Learning.
4. Understand the concepts and operation of support vector machines, kernel SVM, Naive Bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbors, K-means clustering and more.
5. Model a wide variety of robust Machine Learning algorithms including deep learning, clustering, and recommendation systems
The Machine Learning Course is recommended for:
1. Developers aspiring to be a data scientist or Machine Learning engineer
2. Information architects who want to gain expertise in Machine Learning algorithms
3. Analytics professionals who want to work in Machine Learning or artificial intelligence
4. Graduates looking to build a career in data science and Machine Learning
Learn more at: https://www.simplilearn.com/big-data-and-analytics/machine-learning-certification-training-course?utm_campaign=What-is-Machine-Learning-7JhjINPwfYQ&utm_medium=Tutorials&utm_source=youtube
For more updates on courses and tips follow us on:
- Facebook: https://www.facebook.com/Simplilearn
- Twitter: https://twitter.com/simplilearn
- LinkedIn: https://www.linkedin.com/company/simplilearn
- Website: https://www.simplilearn.com
Get the Android app: http://bit.ly/1WlVo4u
Get the iOS app: http://apple.co/1HIO5J0

K mean clustering algorithm with solve example

Take the Full Course of Datawarehouse
What we Provide
1)22 Videos (Index is given down) + Update will be Coming Before final exams
2)Hand made Notes with problems for your to practice
3)Strategy to ScoreGoodMarks in DWM
To buy the course click here: https://goo.gl/to1yMH
or Fill the form we will contact you
https://goo.gl/forms/2SO5NAhqFnjOiWvi2
if you have any query email us at
support@lastmomenttuitions.com
or
lastmomenttuitions@gmail.com
Index
Introduction to Datawarehouse
Meta data in 5 mins
Datamart in datawarehouse
Architecture of datawarehouse
how to draw star schema slowflake schema and fact constelation
what is Olap operation
OLAP vs OLTP
decision tree with solved example
K mean clustering algorithm
Introduction to data mining and architecture
Naive b...

** PythonTraining for Data Science: https://www.edureka.co/python **
This Edureka Machine Learning tutorial (Machine Learning Tutorial with Python Blog: https://goo.gl/fe7ykh ) series presents another video on "K-Means Clustering Algorithm". Within the video you will learn the concepts of K-Means clustering and its implementation using python. Below are the topics covered in today's session:
1. What is Clustering?
2. Types of Clustering
3. What is K-Means Clustering?
4. How does a K-Means Algorithm works?
5. K-Means Clustering Using Python
Machine Learning Tutorial Playlist: https://goo.gl/UxjTxm
Subscribe to our channel to get video updates. Hit the subscribe button above.
How it Works?
1. This is a 5 WeekInstructor led OnlineCourse,40 hours of assignment and 20 hours of project w...

published: 28 May 2018

How kNN algorithm works

In this video I describe how the k Nearest Neighbors algorithm works, and provide a simple example using 2-dimensional data and k = 3.
This presentation is available at: http://prezi.com/ukps8hzjizqw/?utm_campaign=share&utm_medium=copy

published: 18 Feb 2014

Kadane's Algorithm to Maximum Sum Subarray Problem

Here's a quick explanation of Kadane's Algorithm to Maximum Sum Subarray Problem.
This problem, also known as Maximum Subarray Problem, is a very common question in a coding interview, and this gives the optimal answer.

K-means & Image Segmentation - Computerphile

K-means sorts data based on averages. Dr MikePound explains how it works.
FirePong in Detail: https://youtu.be/ZoZMMg1r_Oc
Deep Dream: https://youtu.be/BsSmBPmPeYQ
FPS & Digital Video: https://youtu.be/yniSnYtkrwQ
Dr. Mike's Code:
% This script is the one mentioned during the Computerphile Image
% Segmentation video. I chose matlab because it's a popular tool for
% quickly prototyping things. Matlab licenses are pricey, if you don't have
% one (or, like me, work for an organisation that does) try Octave as a
% good free alternative. This code should work in Octave too.
% Load in an input image
im = imread('C:\Path\Of\Input\Image.jpg');
% In matlab, K-means operates on a 2D array, where each sample is one row,
% and the features are the columns. We can use the reshape function to ...

Lecture 60 — The k Means Algorithm | Stanford University

.
CopyrightDisclaimer Under Section 107 of the Copyright Act 1976, allowance is made for "FAIRUSE" for purposes such as criticism, comment, news reporting, teaching, scholarship, and research. Fair use is a use permitted by copyright statute that might otherwise be infringing. Non-profit, educational or personal use tips the balance in favor of fair use.
.

This week, Ingo Mierswa, RapidMiner's CEO, Co-Founder & DataScientist in Residence, explains how the K-nearest-neighbors algorithm is used to formulate ideas by comparing points in a data-space and using the most similar data points as guidelines for predictions.
Plus, Ingo asks Data Scientist Number 7 to clean up Glen's mess, Doc Brown makes another split-second appearance and we hear the brief chuckle of a Python wearing a tuxedo.
DISCLAIMER: No data scientists were harmed during the filming of this episode. In fact, they were too busy doing amazing things with RapidMiner.
MUSIC CREDITS: Evil Thoughts, The Oscillator, James Taylor Quartet, Real SelfRecords, 2000

published: 28 Jan 2015

K means Clustering Algorithm Explained With an Example Easiest And Quickest Way Ever In Hindi

This KNNAlgorithm tutorial (K-Nearest NeighborClassification Algorithm tutorial) will help you understand what is KNN, why do we need KNN, how do we choose the factor 'K', when do we use KNN, how does KNN algorithm work and you will also see a use case demo showing how to predict whether a person will have diabetes or not using KNN algorithm. KNN algorithm can be applied to both classification and regression problems. Apparently, within the DataScience industry, it's more widely used to solve classification problems. It’s a simple algorithm that stores all available cases and classifies any new cases by taking a majority vote of its k neighbors. Now lets deep dive into this video to understand what is KNN algorithm and how does it actually works.
Below topics are explained in this K-Ne...

K mean clustering algorithm with solve example

Take the Full Course of Datawarehouse
What we Provide
1)22 Videos (Index is given down) + Update will be Coming Before final exams
2)Hand made Notes with pro...

Take the Full Course of Datawarehouse
What we Provide
1)22 Videos (Index is given down) + Update will be Coming Before final exams
2)Hand made Notes with problems for your to practice
3)Strategy to ScoreGoodMarks in DWM
To buy the course click here: https://goo.gl/to1yMH
or Fill the form we will contact you
https://goo.gl/forms/2SO5NAhqFnjOiWvi2
if you have any query email us at
support@lastmomenttuitions.com
or
lastmomenttuitions@gmail.com
Index
Introduction to Datawarehouse
Meta data in 5 mins
Datamart in datawarehouse
Architecture of datawarehouse
how to draw star schema slowflake schema and fact constelation
what is Olap operation
OLAP vs OLTP
decision tree with solved example
K mean clustering algorithm
Introduction to data mining and architecture
Naive bayes classifier
Apriori AlgorithmAgglomerative clustering algorithmn
KDD in data mining
ETL process
FP TREE Algorithm
Decision tree

Take the Full Course of Datawarehouse
What we Provide
1)22 Videos (Index is given down) + Update will be Coming Before final exams
2)Hand made Notes with problems for your to practice
3)Strategy to ScoreGoodMarks in DWM
To buy the course click here: https://goo.gl/to1yMH
or Fill the form we will contact you
https://goo.gl/forms/2SO5NAhqFnjOiWvi2
if you have any query email us at
support@lastmomenttuitions.com
or
lastmomenttuitions@gmail.com
Index
Introduction to Datawarehouse
Meta data in 5 mins
Datamart in datawarehouse
Architecture of datawarehouse
how to draw star schema slowflake schema and fact constelation
what is Olap operation
OLAP vs OLTP
decision tree with solved example
K mean clustering algorithm
Introduction to data mining and architecture
Naive bayes classifier
Apriori AlgorithmAgglomerative clustering algorithmn
KDD in data mining
ETL process
FP TREE Algorithm
Decision tree

** PythonTraining for Data Science: https://www.edureka.co/python **
This Edureka Machine Learning tutorial (Machine Learning Tutorial with Python Blog: https://goo.gl/fe7ykh ) series presents another video on "K-Means Clustering Algorithm". Within the video you will learn the concepts of K-Means clustering and its implementation using python. Below are the topics covered in today's session:
1. What is Clustering?
2. Types of Clustering
3. What is K-Means Clustering?
4. How does a K-Means Algorithm works?
5. K-Means Clustering Using Python
Machine Learning Tutorial Playlist: https://goo.gl/UxjTxm
Subscribe to our channel to get video updates. Hit the subscribe button above.
How it Works?
1. This is a 5 WeekInstructor led OnlineCourse,40 hours of assignment and 20 hours of project work
2. We have a 24x7 One-on-One LIVETechnical Support to help you with any problems you might face or any clarifications you may require during the course.
3. At the end of the training you will be working on a real time project for which we will provide you a Grade and a VerifiableCertificate!
- - - - - - - - - - - - - - - - -
About the Course
Edureka's Python Online Certification Training will make you an expert in Python programming. It will also help you learn Python the Big data way with integration of Machine learning, Pig, Hive and Web Scraping through beautiful soup. During our Python Certification training, our instructors will help you:
1. Programmatically download and analyze data
2. Learn techniques to deal with different types of data – ordinal, categorical, encoding
3. Learn data visualization
4. Using I python notebooks, master the art of presenting step by step data analysis
5. Gain insight into the 'Roles' played by a Machine Learning Engineer
6. Describe Machine Learning
7. Work with real-time data
8. Learn tools and techniques for predictive modeling
9. Discuss Machine Learning algorithms and their implementation
10. Validate Machine Learning algorithms
11. Explain Time Series and its related concepts
12. Perform Text Mining and Sentimental analysis
13. Gain expertise to handle business in future, living the present
- - - - - - - - - - - - - - - - - - -
Why learn Python?
Programmers love Python because of how fast and easy it is to use. Python cuts development time in half with its simple to read syntax and easy compilation feature. Debugging your programs is a breeze in Python with its built in debugger. Using Python makes Programmers more productive and their programs ultimately better. Python continues to be a favorite option for data scientists who use it for building and using Machine learning applications and other scientific computations.
Python runs on Windows, Linux/Unix, Mac OS and has been ported to Java and .NET virtual machines. Python is free to use, even for the commercial products, because of its OSI-approved open source license.
Python has evolved as the most preferred Language for Data Analytics and the increasing search trends on python also indicates that Python is the next "Big Thing" and a must for Professionals in the Data Analytics domain.
For more information, please write back to us at sales@edureka.co
Call us at US: +18336900808 (Toll Free) or India: +918861301699
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
CustomerReview
Sairaam Varadarajan, Data Evangelist at Medtronic, Tempe, Arizona: "I took Big Data and Hadoop / Python course and I am planning to take Apache Mahout thus becoming the "customer of Edureka!". Instructors are knowledge... able and interactive in teaching. The sessions are well structured with a proper content in helping us to dive into Big Data / Python. Most of the online courses are free, edureka charges a minimal amount. Its acceptable for their hard-work in tailoring - All new advanced courses and its specific usage in industry. I am confident that, no other website which have tailored the courses like Edureka. It will help for an immediate take-off in Data Science and Hadoop working."

** PythonTraining for Data Science: https://www.edureka.co/python **
This Edureka Machine Learning tutorial (Machine Learning Tutorial with Python Blog: https://goo.gl/fe7ykh ) series presents another video on "K-Means Clustering Algorithm". Within the video you will learn the concepts of K-Means clustering and its implementation using python. Below are the topics covered in today's session:
1. What is Clustering?
2. Types of Clustering
3. What is K-Means Clustering?
4. How does a K-Means Algorithm works?
5. K-Means Clustering Using Python
Machine Learning Tutorial Playlist: https://goo.gl/UxjTxm
Subscribe to our channel to get video updates. Hit the subscribe button above.
How it Works?
1. This is a 5 WeekInstructor led OnlineCourse,40 hours of assignment and 20 hours of project work
2. We have a 24x7 One-on-One LIVETechnical Support to help you with any problems you might face or any clarifications you may require during the course.
3. At the end of the training you will be working on a real time project for which we will provide you a Grade and a VerifiableCertificate!
- - - - - - - - - - - - - - - - -
About the Course
Edureka's Python Online Certification Training will make you an expert in Python programming. It will also help you learn Python the Big data way with integration of Machine learning, Pig, Hive and Web Scraping through beautiful soup. During our Python Certification training, our instructors will help you:
1. Programmatically download and analyze data
2. Learn techniques to deal with different types of data – ordinal, categorical, encoding
3. Learn data visualization
4. Using I python notebooks, master the art of presenting step by step data analysis
5. Gain insight into the 'Roles' played by a Machine Learning Engineer
6. Describe Machine Learning
7. Work with real-time data
8. Learn tools and techniques for predictive modeling
9. Discuss Machine Learning algorithms and their implementation
10. Validate Machine Learning algorithms
11. Explain Time Series and its related concepts
12. Perform Text Mining and Sentimental analysis
13. Gain expertise to handle business in future, living the present
- - - - - - - - - - - - - - - - - - -
Why learn Python?
Programmers love Python because of how fast and easy it is to use. Python cuts development time in half with its simple to read syntax and easy compilation feature. Debugging your programs is a breeze in Python with its built in debugger. Using Python makes Programmers more productive and their programs ultimately better. Python continues to be a favorite option for data scientists who use it for building and using Machine learning applications and other scientific computations.
Python runs on Windows, Linux/Unix, Mac OS and has been ported to Java and .NET virtual machines. Python is free to use, even for the commercial products, because of its OSI-approved open source license.
Python has evolved as the most preferred Language for Data Analytics and the increasing search trends on python also indicates that Python is the next "Big Thing" and a must for Professionals in the Data Analytics domain.
For more information, please write back to us at sales@edureka.co
Call us at US: +18336900808 (Toll Free) or India: +918861301699
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
CustomerReview
Sairaam Varadarajan, Data Evangelist at Medtronic, Tempe, Arizona: "I took Big Data and Hadoop / Python course and I am planning to take Apache Mahout thus becoming the "customer of Edureka!". Instructors are knowledge... able and interactive in teaching. The sessions are well structured with a proper content in helping us to dive into Big Data / Python. Most of the online courses are free, edureka charges a minimal amount. Its acceptable for their hard-work in tailoring - All new advanced courses and its specific usage in industry. I am confident that, no other website which have tailored the courses like Edureka. It will help for an immediate take-off in Data Science and Hadoop working."

How kNN algorithm works

In this video I describe how the k Nearest Neighbors algorithm works, and provide a simple example using 2-dimensional data and k = 3.
This presentation is ava...

In this video I describe how the k Nearest Neighbors algorithm works, and provide a simple example using 2-dimensional data and k = 3.
This presentation is available at: http://prezi.com/ukps8hzjizqw/?utm_campaign=share&utm_medium=copy

In this video I describe how the k Nearest Neighbors algorithm works, and provide a simple example using 2-dimensional data and k = 3.
This presentation is available at: http://prezi.com/ukps8hzjizqw/?utm_campaign=share&utm_medium=copy

Here's a quick explanation of Kadane's Algorithm to Maximum Sum Subarray Problem.
This problem, also known as Maximum Subarray Problem, is a very common question in a coding interview, and this gives the optimal answer.

Here's a quick explanation of Kadane's Algorithm to Maximum Sum Subarray Problem.
This problem, also known as Maximum Subarray Problem, is a very common question in a coding interview, and this gives the optimal answer.

K-means sorts data based on averages. Dr MikePound explains how it works.
FirePong in Detail: https://youtu.be/ZoZMMg1r_Oc
Deep Dream: https://youtu.be/BsSmBPmPeYQ
FPS & Digital Video: https://youtu.be/yniSnYtkrwQ
Dr. Mike's Code:
% This script is the one mentioned during the Computerphile Image
% Segmentation video. I chose matlab because it's a popular tool for
% quickly prototyping things. Matlab licenses are pricey, if you don't have
% one (or, like me, work for an organisation that does) try Octave as a
% good free alternative. This code should work in Octave too.
% Load in an input image
im = imread('C:\Path\Of\Input\Image.jpg');
% In matlab, K-means operates on a 2D array, where each sample is one row,
% and the features are the columns. We can use the reshape function to turn
% the image into this format, where each pixel is one row, and R,G and B
% are the columns. We are turning a W,H,3 image into W*H,3
% We also cast to a double array, because K-means requires it in matlab
imflat = double(reshape(im, size(im,1) * size(im,2), 3));
% I specify that initialisation shuold sample points at
% random, rather than anything complex like kmeans++ initialisation.
% Kmeans++ takes a long time if you are using 256 classes.
% Perform k-means. This function returns the class IDs assigned to each
% pixel, and in this case we also want the mean values for each class -
% what colour is each class. This can take a long time if the value for K
% is large, I've used the sampling start strategy to speed things up.
% While KMeans is running, it will show you the iteration count, and the
% number of pixels that have changed class since last iteration. This
% number should get lower and lower, as the means settle on appropriate
% values. For large K, it's unlikely that we will ever reach zero movement
% (convergence) within 150 iterations.
K = 3
[kIDs, kC] = kmeans(imflat, K, 'Display', 'iter', 'MaxIter', 150, 'Start', 'sample');
% Matlab can output paletted images, that is, grayscale images where the
% colours are stored in a separate array. This array is kC, and kIDs are
% the grayscale indices.
colormap = kC / 256; % Scale0-1, since this is what matlab wants
% Reshape kIDs back into the original image shape
imout = reshape(uint8(kIDs), size(im,1), size(im,2));
% Save file out, you need to subtract 1 from the image classes, since once
% stored in the file the values should go from 0-255, not 1-256 like matlab
% would do.
imwrite(imout - 1, colormap, 'C:\Path\Of\Output\Image.png');
http://www.facebook.com/computerphile
https://twitter.com/computer_phile
This video was filmed and edited by Sean Riley.
Computer Science at the University of Nottingham: http://bit.ly/nottscomputer
Computerphile is a sister project to Brady Haran's Numberphile. More at http://www.bradyharan.com

K-means sorts data based on averages. Dr MikePound explains how it works.
FirePong in Detail: https://youtu.be/ZoZMMg1r_Oc
Deep Dream: https://youtu.be/BsSmBPmPeYQ
FPS & Digital Video: https://youtu.be/yniSnYtkrwQ
Dr. Mike's Code:
% This script is the one mentioned during the Computerphile Image
% Segmentation video. I chose matlab because it's a popular tool for
% quickly prototyping things. Matlab licenses are pricey, if you don't have
% one (or, like me, work for an organisation that does) try Octave as a
% good free alternative. This code should work in Octave too.
% Load in an input image
im = imread('C:\Path\Of\Input\Image.jpg');
% In matlab, K-means operates on a 2D array, where each sample is one row,
% and the features are the columns. We can use the reshape function to turn
% the image into this format, where each pixel is one row, and R,G and B
% are the columns. We are turning a W,H,3 image into W*H,3
% We also cast to a double array, because K-means requires it in matlab
imflat = double(reshape(im, size(im,1) * size(im,2), 3));
% I specify that initialisation shuold sample points at
% random, rather than anything complex like kmeans++ initialisation.
% Kmeans++ takes a long time if you are using 256 classes.
% Perform k-means. This function returns the class IDs assigned to each
% pixel, and in this case we also want the mean values for each class -
% what colour is each class. This can take a long time if the value for K
% is large, I've used the sampling start strategy to speed things up.
% While KMeans is running, it will show you the iteration count, and the
% number of pixels that have changed class since last iteration. This
% number should get lower and lower, as the means settle on appropriate
% values. For large K, it's unlikely that we will ever reach zero movement
% (convergence) within 150 iterations.
K = 3
[kIDs, kC] = kmeans(imflat, K, 'Display', 'iter', 'MaxIter', 150, 'Start', 'sample');
% Matlab can output paletted images, that is, grayscale images where the
% colours are stored in a separate array. This array is kC, and kIDs are
% the grayscale indices.
colormap = kC / 256; % Scale0-1, since this is what matlab wants
% Reshape kIDs back into the original image shape
imout = reshape(uint8(kIDs), size(im,1), size(im,2));
% Save file out, you need to subtract 1 from the image classes, since once
% stored in the file the values should go from 0-255, not 1-256 like matlab
% would do.
imwrite(imout - 1, colormap, 'C:\Path\Of\Output\Image.png');
http://www.facebook.com/computerphile
https://twitter.com/computer_phile
This video was filmed and edited by Sean Riley.
Computer Science at the University of Nottingham: http://bit.ly/nottscomputer
Computerphile is a sister project to Brady Haran's Numberphile. More at http://www.bradyharan.com

.
CopyrightDisclaimer Under Section 107 of the Copyright Act 1976, allowance is made for "FAIRUSE" for purposes such as criticism, comment, news reporting, teaching, scholarship, and research. Fair use is a use permitted by copyright statute that might otherwise be infringing. Non-profit, educational or personal use tips the balance in favor of fair use.
.

.
CopyrightDisclaimer Under Section 107 of the Copyright Act 1976, allowance is made for "FAIRUSE" for purposes such as criticism, comment, news reporting, teaching, scholarship, and research. Fair use is a use permitted by copyright statute that might otherwise be infringing. Non-profit, educational or personal use tips the balance in favor of fair use.
.

( Data ScienceTraining - https://www.edureka.co/data-science )
This Edureka k-means clustering algorithm tutorial video (Data Science Blog Series: https://goo.gl/6ojfAa) will take you through the machine learning introduction, cluster analysis, types of clustering algorithms, k-means clustering, how it works along with an example/ demo in R. This Data Science with R tutorial video is ideal for beginners to learn how k-means clustering work. You can also read the blog here: https://goo.gl/QM8on4
Subscribe to our channel to get video updates. Hit the subscribe button above.
Check our complete Data Science playlist here: https://goo.gl/60NJJS
#kmeans #clusteranalysis #clustering #datascience #machinelearning
How it Works?
1. There will be 30 hours of instructor-led interactive online classes, 40 hours of assignments and 20 hours of project
2. We have a 24x7 One-on-One LIVETechnical Support to help you with any problems you might face or any clarifications you may require during the course.
3. You will get LifetimeAccess to the recordings in the LMS.
4. At the end of the training you will have to complete the project based on which we will provide you a VerifiableCertificate!
- - - - - - - - - - - - - -
About the Course
Edureka's Data Science course will cover the whole data life cycle ranging from Data Acquisition and Data Storage using R-Hadoop concepts, Applying modelling through R programming using Machine learning algorithms and illustrate impeccable Data Visualization by leveraging on 'R' capabilities.
- - - - - - - - - - - - - -
Why Learn Data Science?
Data Science training certifies you with ‘in demand’ Big Data Technologies to help you grab the top paying Data Science job title with Big Data skills and expertise in R programming, Machine Learning and Hadoop framework.
After the completion of the DataScience course, you should be able to:
1. Gain insight into the 'Roles' played by a DataScientist
2. Analyse Big Data using R, Hadoop and Machine Learning
3. Understand the Data AnalysisLife Cycle
4. Work with different data formats like XML, CSV and SAS, SPSS, etc.
5. Learn tools and techniques for data transformation
6. Understand Data Mining techniques and their implementation
7. Analyse data using machine learning algorithms in R
8. Work with Hadoop Mappers and Reducers to analyze data
9. Implement various Machine Learning Algorithms in Apache Mahout
10. Gain insight into data visualization and optimization techniques
11. Explore the parallel processing feature in R
- - - - - - - - - - - - - -
Who should go for this course?
The course is designed for all those who want to learn machine learning techniques with implementation in R language, and wish to apply these techniques on Big Data. The following professionals can go for this course:
1. Developers aspiring to be a 'Data Scientist'
2. Analytics Managers who are leading a team of analysts
3. SAS/SPSS Professionals looking to gain understanding in Big Data Analytics
4. Business Analysts who want to understand Machine Learning (ML) Techniques
5. InformationArchitects who want to gain expertise in Predictive Analytics
6. 'R' professionals who want to captivate and analyze Big Data
7. Hadoop Professionals who want to learn R and ML techniques
8. Analysts wanting to understand Data Science methodologies
Please write back to us at sales@edureka.co or call us at +918880862004 or 18002759730 for more information.
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
Customer Reviews:
Gnana Sekhar Vangara, TechnologyLead at WellsFargo.com, says, "Edureka Data science course provided me a very good mixture of theoretical and practical training. The training course helped me in all areas that I was previously unclear about, especially concepts like Machine learning and Mahout. The training was very informative and practical. LMS pre recorded sessions and assignmemts were very good as there is a lot of information in them that will help me in my job. The trainer was able to explain difficult to understand subjects in simple terms. Edureka is my teaching GURU now...Thanks EDUREKA and all the best. "

( Data ScienceTraining - https://www.edureka.co/data-science )
This Edureka k-means clustering algorithm tutorial video (Data Science Blog Series: https://goo.gl/6ojfAa) will take you through the machine learning introduction, cluster analysis, types of clustering algorithms, k-means clustering, how it works along with an example/ demo in R. This Data Science with R tutorial video is ideal for beginners to learn how k-means clustering work. You can also read the blog here: https://goo.gl/QM8on4
Subscribe to our channel to get video updates. Hit the subscribe button above.
Check our complete Data Science playlist here: https://goo.gl/60NJJS
#kmeans #clusteranalysis #clustering #datascience #machinelearning
How it Works?
1. There will be 30 hours of instructor-led interactive online classes, 40 hours of assignments and 20 hours of project
2. We have a 24x7 One-on-One LIVETechnical Support to help you with any problems you might face or any clarifications you may require during the course.
3. You will get LifetimeAccess to the recordings in the LMS.
4. At the end of the training you will have to complete the project based on which we will provide you a VerifiableCertificate!
- - - - - - - - - - - - - -
About the Course
Edureka's Data Science course will cover the whole data life cycle ranging from Data Acquisition and Data Storage using R-Hadoop concepts, Applying modelling through R programming using Machine learning algorithms and illustrate impeccable Data Visualization by leveraging on 'R' capabilities.
- - - - - - - - - - - - - -
Why Learn Data Science?
Data Science training certifies you with ‘in demand’ Big Data Technologies to help you grab the top paying Data Science job title with Big Data skills and expertise in R programming, Machine Learning and Hadoop framework.
After the completion of the DataScience course, you should be able to:
1. Gain insight into the 'Roles' played by a DataScientist
2. Analyse Big Data using R, Hadoop and Machine Learning
3. Understand the Data AnalysisLife Cycle
4. Work with different data formats like XML, CSV and SAS, SPSS, etc.
5. Learn tools and techniques for data transformation
6. Understand Data Mining techniques and their implementation
7. Analyse data using machine learning algorithms in R
8. Work with Hadoop Mappers and Reducers to analyze data
9. Implement various Machine Learning Algorithms in Apache Mahout
10. Gain insight into data visualization and optimization techniques
11. Explore the parallel processing feature in R
- - - - - - - - - - - - - -
Who should go for this course?
The course is designed for all those who want to learn machine learning techniques with implementation in R language, and wish to apply these techniques on Big Data. The following professionals can go for this course:
1. Developers aspiring to be a 'Data Scientist'
2. Analytics Managers who are leading a team of analysts
3. SAS/SPSS Professionals looking to gain understanding in Big Data Analytics
4. Business Analysts who want to understand Machine Learning (ML) Techniques
5. InformationArchitects who want to gain expertise in Predictive Analytics
6. 'R' professionals who want to captivate and analyze Big Data
7. Hadoop Professionals who want to learn R and ML techniques
8. Analysts wanting to understand Data Science methodologies
Please write back to us at sales@edureka.co or call us at +918880862004 or 18002759730 for more information.
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
Customer Reviews:
Gnana Sekhar Vangara, TechnologyLead at WellsFargo.com, says, "Edureka Data science course provided me a very good mixture of theoretical and practical training. The training course helped me in all areas that I was previously unclear about, especially concepts like Machine learning and Mahout. The training was very informative and practical. LMS pre recorded sessions and assignmemts were very good as there is a lot of information in them that will help me in my job. The trainer was able to explain difficult to understand subjects in simple terms. Edureka is my teaching GURU now...Thanks EDUREKA and all the best. "

This week, Ingo Mierswa, RapidMiner's CEO, Co-Founder & DataScientist in Residence, explains how the K-nearest-neighbors algorithm is used to formulate ideas b...

This week, Ingo Mierswa, RapidMiner's CEO, Co-Founder & DataScientist in Residence, explains how the K-nearest-neighbors algorithm is used to formulate ideas by comparing points in a data-space and using the most similar data points as guidelines for predictions.
Plus, Ingo asks Data Scientist Number 7 to clean up Glen's mess, Doc Brown makes another split-second appearance and we hear the brief chuckle of a Python wearing a tuxedo.
DISCLAIMER: No data scientists were harmed during the filming of this episode. In fact, they were too busy doing amazing things with RapidMiner.
MUSIC CREDITS: Evil Thoughts, The Oscillator, James Taylor Quartet, Real SelfRecords, 2000

This week, Ingo Mierswa, RapidMiner's CEO, Co-Founder & DataScientist in Residence, explains how the K-nearest-neighbors algorithm is used to formulate ideas by comparing points in a data-space and using the most similar data points as guidelines for predictions.
Plus, Ingo asks Data Scientist Number 7 to clean up Glen's mess, Doc Brown makes another split-second appearance and we hear the brief chuckle of a Python wearing a tuxedo.
DISCLAIMER: No data scientists were harmed during the filming of this episode. In fact, they were too busy doing amazing things with RapidMiner.
MUSIC CREDITS: Evil Thoughts, The Oscillator, James Taylor Quartet, Real SelfRecords, 2000

This KNNAlgorithm tutorial (K-Nearest NeighborClassification Algorithm tutorial) will help you understand what is KNN, why do we need KNN, how do we choose the factor 'K', when do we use KNN, how does KNN algorithm work and you will also see a use case demo showing how to predict whether a person will have diabetes or not using KNN algorithm. KNN algorithm can be applied to both classification and regression problems. Apparently, within the DataScience industry, it's more widely used to solve classification problems. It’s a simple algorithm that stores all available cases and classifies any new cases by taking a majority vote of its k neighbors. Now lets deep dive into this video to understand what is KNN algorithm and how does it actually works.
Below topics are explained in this K-Nearest Neighbor Classification Algorithm (KNN Algorithm) tutorial:
1. Why do we need KNN?
2. What is KNN?
3. How do we choose the factor 'K'?
4. When do we use KNN?
5. How does KNN algorithm work?
6. Use case - Predict whether a person will have diabetes or not
To learn more about Machine Learning, subscribe to our YouTube channel: https://www.youtube.com/user/Simplilearn?sub_confirmation=1
You can also go through the slides here: https://goo.gl/XP6xcp
Watch more videos on Machine Learning: https://www.youtube.com/watch?v=7JhjINPwfYQ&list=PLEiEAq2VkUULYYgj13YHUWmRePqiu8Ddy
#MachineLearningAlgorithms #Datasciencecourse #datascience #SimplilearnMachineLearning #MachineLearningCourse
Simplilearn’s Machine Learning course will make you an expert in Machine Learning, a form of Artificial Intelligence that automates data analysis to enable computers to learn and adapt through experience to do specific tasks without explicit programming. You will master Machine Learning concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, hands-on modeling to develop algorithms and prepare you for the role of Machine Learning Engineer
Why learn Machine Learning?
Machine Learning is rapidly being deployed in all kinds of industries, creating a huge demand for skilled professionals. The MachineLearning market size is expected to grow from USD 1.03 billion in 2016 to USD 8.81 billion by 2022, at a CompoundAnnualGrowth Rate (CAGR) of 44.1% during the forecast period.
You can gain in-depth knowledge of Machine Learning by taking our Machine Learning certification training course. With Simplilearn’s Machine Learning course, you will prepare for a career as a Machine Learning engineer as you master concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, and hands-on modeling to develop algorithms. Those who complete the course will be able to:
1. Master the concepts of supervised, unsupervised and reinforcement learning concepts and modeling.
2. Gain practical mastery over principles, algorithms, and applications of Machine Learning through a hands-on approach which includes working on 28 projects and one capstone project.
3. Acquire thorough knowledge of the mathematical and heuristic aspects of Machine Learning.
4. Understand the concepts and operation of support vector machines, kernel SVM, Naive Bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbors, K-means clustering and more.
5. Model a wide variety of robust Machine Learning algorithms including deep learning, clustering, and recommendation systems
The Machine Learning Course is recommended for:
1. Developers aspiring to be a data scientist or Machine Learning engineer
2. Information architects who want to gain expertise in Machine Learning algorithms
3. Analytics professionals who want to work in Machine Learning or artificial intelligence
4. Graduates looking to build a career in data science and Machine Learning
Learn more at: https://www.simplilearn.com/big-data-and-analytics/machine-learning-certification-training-course?utm_campaign=What-is-Machine-Learning-7JhjINPwfYQ&utm_medium=Tutorials&utm_source=youtube
For more updates on courses and tips follow us on:
- Facebook: https://www.facebook.com/Simplilearn
- Twitter: https://twitter.com/simplilearn
- LinkedIn: https://www.linkedin.com/company/simplilearn
- Website: https://www.simplilearn.com
Get the Android app: http://bit.ly/1WlVo4u
Get the iOS app: http://apple.co/1HIO5J0

This KNNAlgorithm tutorial (K-Nearest NeighborClassification Algorithm tutorial) will help you understand what is KNN, why do we need KNN, how do we choose the factor 'K', when do we use KNN, how does KNN algorithm work and you will also see a use case demo showing how to predict whether a person will have diabetes or not using KNN algorithm. KNN algorithm can be applied to both classification and regression problems. Apparently, within the DataScience industry, it's more widely used to solve classification problems. It’s a simple algorithm that stores all available cases and classifies any new cases by taking a majority vote of its k neighbors. Now lets deep dive into this video to understand what is KNN algorithm and how does it actually works.
Below topics are explained in this K-Nearest Neighbor Classification Algorithm (KNN Algorithm) tutorial:
1. Why do we need KNN?
2. What is KNN?
3. How do we choose the factor 'K'?
4. When do we use KNN?
5. How does KNN algorithm work?
6. Use case - Predict whether a person will have diabetes or not
To learn more about Machine Learning, subscribe to our YouTube channel: https://www.youtube.com/user/Simplilearn?sub_confirmation=1
You can also go through the slides here: https://goo.gl/XP6xcp
Watch more videos on Machine Learning: https://www.youtube.com/watch?v=7JhjINPwfYQ&list=PLEiEAq2VkUULYYgj13YHUWmRePqiu8Ddy
#MachineLearningAlgorithms #Datasciencecourse #datascience #SimplilearnMachineLearning #MachineLearningCourse
Simplilearn’s Machine Learning course will make you an expert in Machine Learning, a form of Artificial Intelligence that automates data analysis to enable computers to learn and adapt through experience to do specific tasks without explicit programming. You will master Machine Learning concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, hands-on modeling to develop algorithms and prepare you for the role of Machine Learning Engineer
Why learn Machine Learning?
Machine Learning is rapidly being deployed in all kinds of industries, creating a huge demand for skilled professionals. The MachineLearning market size is expected to grow from USD 1.03 billion in 2016 to USD 8.81 billion by 2022, at a CompoundAnnualGrowth Rate (CAGR) of 44.1% during the forecast period.
You can gain in-depth knowledge of Machine Learning by taking our Machine Learning certification training course. With Simplilearn’s Machine Learning course, you will prepare for a career as a Machine Learning engineer as you master concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, and hands-on modeling to develop algorithms. Those who complete the course will be able to:
1. Master the concepts of supervised, unsupervised and reinforcement learning concepts and modeling.
2. Gain practical mastery over principles, algorithms, and applications of Machine Learning through a hands-on approach which includes working on 28 projects and one capstone project.
3. Acquire thorough knowledge of the mathematical and heuristic aspects of Machine Learning.
4. Understand the concepts and operation of support vector machines, kernel SVM, Naive Bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbors, K-means clustering and more.
5. Model a wide variety of robust Machine Learning algorithms including deep learning, clustering, and recommendation systems
The Machine Learning Course is recommended for:
1. Developers aspiring to be a data scientist or Machine Learning engineer
2. Information architects who want to gain expertise in Machine Learning algorithms
3. Analytics professionals who want to work in Machine Learning or artificial intelligence
4. Graduates looking to build a career in data science and Machine Learning
Learn more at: https://www.simplilearn.com/big-data-and-analytics/machine-learning-certification-training-course?utm_campaign=What-is-Machine-Learning-7JhjINPwfYQ&utm_medium=Tutorials&utm_source=youtube
For more updates on courses and tips follow us on:
- Facebook: https://www.facebook.com/Simplilearn
- Twitter: https://twitter.com/simplilearn
- LinkedIn: https://www.linkedin.com/company/simplilearn
- Website: https://www.simplilearn.com
Get the Android app: http://bit.ly/1WlVo4u
Get the iOS app: http://apple.co/1HIO5J0

K mean clustering algorithm with solve example

Take the Full Course of Datawarehouse
What we Provide
1)22 Videos (Index is given down) + Update will be Coming Before final exams
2)Hand made Notes with problems for your to practice
3)Strategy to ScoreGoodMarks in DWM
To buy the course click here: https://goo.gl/to1yMH
or Fill the form we will contact you
https://goo.gl/forms/2SO5NAhqFnjOiWvi2
if you have any query email us at
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or
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Index
Introduction to Datawarehouse
Meta data in 5 mins
Datamart in datawarehouse
Architecture of datawarehouse
how to draw star schema slowflake schema and fact constelation
what is Olap operation
OLAP vs OLTP
decision tree with solved example
K mean clustering algorithm
Introduction to data mining and architecture
Naive bayes classifier
Apriori AlgorithmAgglomerative clustering algorithmn
KDD in data mining
ETL process
FP TREE Algorithm
Decision tree

** PythonTraining for Data Science: https://www.edureka.co/python **
This Edureka Machine Learning tutorial (Machine Learning Tutorial with Python Blog: https://goo.gl/fe7ykh ) series presents another video on "K-Means Clustering Algorithm". Within the video you will learn the concepts of K-Means clustering and its implementation using python. Below are the topics covered in today's session:
1. What is Clustering?
2. Types of Clustering
3. What is K-Means Clustering?
4. How does a K-Means Algorithm works?
5. K-Means Clustering Using Python
Machine Learning Tutorial Playlist: https://goo.gl/UxjTxm
Subscribe to our channel to get video updates. Hit the subscribe button above.
How it Works?
1. This is a 5 WeekInstructor led OnlineCourse,40 hours of assignment and 20 hours of project work
2. We have a 24x7 One-on-One LIVETechnical Support to help you with any problems you might face or any clarifications you may require during the course.
3. At the end of the training you will be working on a real time project for which we will provide you a Grade and a VerifiableCertificate!
- - - - - - - - - - - - - - - - -
About the Course
Edureka's Python Online Certification Training will make you an expert in Python programming. It will also help you learn Python the Big data way with integration of Machine learning, Pig, Hive and Web Scraping through beautiful soup. During our Python Certification training, our instructors will help you:
1. Programmatically download and analyze data
2. Learn techniques to deal with different types of data – ordinal, categorical, encoding
3. Learn data visualization
4. Using I python notebooks, master the art of presenting step by step data analysis
5. Gain insight into the 'Roles' played by a Machine Learning Engineer
6. Describe Machine Learning
7. Work with real-time data
8. Learn tools and techniques for predictive modeling
9. Discuss Machine Learning algorithms and their implementation
10. Validate Machine Learning algorithms
11. Explain Time Series and its related concepts
12. Perform Text Mining and Sentimental analysis
13. Gain expertise to handle business in future, living the present
- - - - - - - - - - - - - - - - - - -
Why learn Python?
Programmers love Python because of how fast and easy it is to use. Python cuts development time in half with its simple to read syntax and easy compilation feature. Debugging your programs is a breeze in Python with its built in debugger. Using Python makes Programmers more productive and their programs ultimately better. Python continues to be a favorite option for data scientists who use it for building and using Machine learning applications and other scientific computations.
Python runs on Windows, Linux/Unix, Mac OS and has been ported to Java and .NET virtual machines. Python is free to use, even for the commercial products, because of its OSI-approved open source license.
Python has evolved as the most preferred Language for Data Analytics and the increasing search trends on python also indicates that Python is the next "Big Thing" and a must for Professionals in the Data Analytics domain.
For more information, please write back to us at sales@edureka.co
Call us at US: +18336900808 (Toll Free) or India: +918861301699
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
CustomerReview
Sairaam Varadarajan, Data Evangelist at Medtronic, Tempe, Arizona: "I took Big Data and Hadoop / Python course and I am planning to take Apache Mahout thus becoming the "customer of Edureka!". Instructors are knowledge... able and interactive in teaching. The sessions are well structured with a proper content in helping us to dive into Big Data / Python. Most of the online courses are free, edureka charges a minimal amount. Its acceptable for their hard-work in tailoring - All new advanced courses and its specific usage in industry. I am confident that, no other website which have tailored the courses like Edureka. It will help for an immediate take-off in Data Science and Hadoop working."

How kNN algorithm works

In this video I describe how the k Nearest Neighbors algorithm works, and provide a simple example using 2-dimensional data and k = 3.
This presentation is available at: http://prezi.com/ukps8hzjizqw/?utm_campaign=share&utm_medium=copy

Kadane's Algorithm to Maximum Sum Subarray Problem

Here's a quick explanation of Kadane's Algorithm to Maximum Sum Subarray Problem.
This problem, also known as Maximum Subarray Problem, is a very common question in a coding interview, and this gives the optimal answer.

K-means & Image Segmentation - Computerphile

K-means sorts data based on averages. Dr MikePound explains how it works.
FirePong in Detail: https://youtu.be/ZoZMMg1r_Oc
Deep Dream: https://youtu.be/BsSmBPmPeYQ
FPS & Digital Video: https://youtu.be/yniSnYtkrwQ
Dr. Mike's Code:
% This script is the one mentioned during the Computerphile Image
% Segmentation video. I chose matlab because it's a popular tool for
% quickly prototyping things. Matlab licenses are pricey, if you don't have
% one (or, like me, work for an organisation that does) try Octave as a
% good free alternative. This code should work in Octave too.
% Load in an input image
im = imread('C:\Path\Of\Input\Image.jpg');
% In matlab, K-means operates on a 2D array, where each sample is one row,
% and the features are the columns. We can use the reshape function to turn
% the image into this format, where each pixel is one row, and R,G and B
% are the columns. We are turning a W,H,3 image into W*H,3
% We also cast to a double array, because K-means requires it in matlab
imflat = double(reshape(im, size(im,1) * size(im,2), 3));
% I specify that initialisation shuold sample points at
% random, rather than anything complex like kmeans++ initialisation.
% Kmeans++ takes a long time if you are using 256 classes.
% Perform k-means. This function returns the class IDs assigned to each
% pixel, and in this case we also want the mean values for each class -
% what colour is each class. This can take a long time if the value for K
% is large, I've used the sampling start strategy to speed things up.
% While KMeans is running, it will show you the iteration count, and the
% number of pixels that have changed class since last iteration. This
% number should get lower and lower, as the means settle on appropriate
% values. For large K, it's unlikely that we will ever reach zero movement
% (convergence) within 150 iterations.
K = 3
[kIDs, kC] = kmeans(imflat, K, 'Display', 'iter', 'MaxIter', 150, 'Start', 'sample');
% Matlab can output paletted images, that is, grayscale images where the
% colours are stored in a separate array. This array is kC, and kIDs are
% the grayscale indices.
colormap = kC / 256; % Scale0-1, since this is what matlab wants
% Reshape kIDs back into the original image shape
imout = reshape(uint8(kIDs), size(im,1), size(im,2));
% Save file out, you need to subtract 1 from the image classes, since once
% stored in the file the values should go from 0-255, not 1-256 like matlab
% would do.
imwrite(imout - 1, colormap, 'C:\Path\Of\Output\Image.png');
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Lecture 60 — The k Means Algorithm | Stanford University

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This Edureka k-means clustering algorithm tutorial video (Data Science Blog Series: https://goo.gl/6ojfAa) will take you through the machine learning introduction, cluster analysis, types of clustering algorithms, k-means clustering, how it works along with an example/ demo in R. This Data Science with R tutorial video is ideal for beginners to learn how k-means clustering work. You can also read the blog here: https://goo.gl/QM8on4
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This week, Ingo Mierswa, RapidMiner's CEO, Co-Founder & DataScientist in Residence, explains how the K-nearest-neighbors algorithm is used to formulate ideas by comparing points in a data-space and using the most similar data points as guidelines for predictions.
Plus, Ingo asks Data Scientist Number 7 to clean up Glen's mess, Doc Brown makes another split-second appearance and we hear the brief chuckle of a Python wearing a tuxedo.
DISCLAIMER: No data scientists were harmed during the filming of this episode. In fact, they were too busy doing amazing things with RapidMiner.
MUSIC CREDITS: Evil Thoughts, The Oscillator, James Taylor Quartet, Real SelfRecords, 2000

This KNNAlgorithm tutorial (K-Nearest NeighborClassification Algorithm tutorial) will help you understand what is KNN, why do we need KNN, how do we choose the factor 'K', when do we use KNN, how does KNN algorithm work and you will also see a use case demo showing how to predict whether a person will have diabetes or not using KNN algorithm. KNN algorithm can be applied to both classification and regression problems. Apparently, within the DataScience industry, it's more widely used to solve classification problems. It’s a simple algorithm that stores all available cases and classifies any new cases by taking a majority vote of its k neighbors. Now lets deep dive into this video to understand what is KNN algorithm and how does it actually works.
Below topics are explained in this K-Nearest Neighbor Classification Algorithm (KNN Algorithm) tutorial:
1. Why do we need KNN?
2. What is KNN?
3. How do we choose the factor 'K'?
4. When do we use KNN?
5. How does KNN algorithm work?
6. Use case - Predict whether a person will have diabetes or not
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